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Awesome Open Source


TensorRTx aims to implement popular deep learning networks with tensorrt network definition APIs. As we know, tensorrt has builtin parsers, including caffeparser, uffparser, onnxparser, etc. But when we use these parsers, we often run into some "unsupported operations or layers" problems, especially some state-of-the-art models are using new type of layers.

So why don't we just skip all parsers? We just use TensorRT network definition APIs to build the whole network, it's not so complicated.

I wrote this project to get familiar with tensorrt API, and also to share and learn from the community.

All the models are implemented in pytorch/mxnet/tensorflown first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference. Some pytorch implementations can be found in my repo Pytorchx, the remaining are from polular open-source implementations.


  • 31 Aug 2021. FamousDirector: update retinaface to support TensorRT 8.0.
  • 27 Aug 2021. HaiyangPeng: add a python wrapper for hrnet segmentation.
  • 1 Jul 2021. freedenS: DE⫶TR: End-to-End Object Detection with Transformers. First Transformer model!
  • 10 Jun 2021. upczww: EfficientNet b0-b8 and l2.
  • 23 May 2021. SsisyphusTao: CenterNet DLA-34 with DCNv2 plugin.
  • 17 May 2021. ybw108: arcface LResNet100E-IR and MobileFaceNet.
  • 6 May 2021. makaveli10: scaled-yolov4 yolov4-csp.
  • 29 Apr 2021. upczww: hrnet segmentation w18/w32/w48, ocr branch also.
  • 28 Apr 2021. aditya-dl: mobilenetv2, alexnet, densenet121, mobilenetv3 with python API.
  • 26 Apr 2021. makaveli10 add Inceptionv4.
  • 25 Apr 2021. YOLOv5 updated to v5.0, supporting s/m/l/x/s6/m6/l6/x6.
  • 23 Apr 2021. irvingzhang0512 add TSM: Temporal Shift Module for Efficient Video Understanding, ICCV2019.
  • 23 Apr 2021. freedenS implement MaskRCNN, till now the MOST complicated model in this repo.
  • 16 Apr 2021. irvingzhang0512 implement lenet and resnet50 with Python API, freedenS implement FasterRCNN with five plugins, cheers!
  • 2 Apr 2021. mingyu6yang added a python wrapper for retinaface, makaveli10 added DenseNet-121.


Test Environment

  1. GTX1080 / Ubuntu16.04 / cuda10.0 / cudnn7.6.5 / tensorrt7.0.0 / nvinfer7.0.0 / opencv3.3

How to run

Each folder has a readme inside, which explains how to run the models inside.


Following models are implemented.

Name Description
lenet the simplest, as a "hello world" of this project
alexnet easy to implement, all layers are supported in tensorrt
googlenet GoogLeNet (Inception v1)
inception Inception v3, v4
mnasnet MNASNet with depth multiplier of 0.5 from the paper
mobilenet MobileNet v2, v3-small, v3-large
resnet resnet-18, resnet-50 and resnext50-32x4d are implemented
senet se-resnet50
shufflenet ShuffleNet v2 with 0.5x output channels
squeezenet SqueezeNet 1.1 model
vgg VGG 11-layer model
yolov3-tiny weights and pytorch implementation from ultralytics/yolov3
yolov3 darknet-53, weights and pytorch implementation from ultralytics/yolov3
yolov3-spp darknet-53, weights and pytorch implementation from ultralytics/yolov3
yolov4 CSPDarknet53, weights from AlexeyAB/darknet, pytorch implementation from ultralytics/yolov3
yolov5 yolov5 v1.0-v5.0, pytorch implementation from ultralytics/yolov5
retinaface resnet50 and mobilnet0.25, weights from biubug6/Pytorch_Retinaface
arcface LResNet50E-IR, LResNet100E-IR and MobileFaceNet, weights from deepinsight/insightface
retinafaceAntiCov mobilenet0.25, weights from deepinsight/insightface, retinaface anti-COVID-19, detect face and mask attribute
dbnet Scene Text Detection, weights from BaofengZan/DBNet.pytorch
crnn pytorch implementation from meijieru/crnn.pytorch
ufld pytorch implementation from Ultra-Fast-Lane-Detection, ECCV2020
hrnet hrnet-image-classification and hrnet-semantic-segmentation, pytorch implementation from HRNet-Image-Classification and HRNet-Semantic-Segmentation
psenet PSENet Text Detection, tensorflow implementation from liuheng92/tensorflow_PSENet
ibnnet IBN-Net, pytorch implementation from XingangPan/IBN-Net, ECCV2018
unet U-Net, pytorch implementation from milesial/Pytorch-UNet
repvgg RepVGG, pytorch implementation from DingXiaoH/RepVGG
lprnet LPRNet, pytorch implementation from xuexingyu24/License_Plate_Detection_Pytorch
refinedet RefineDet, pytorch implementation from luuuyi/RefineDet.PyTorch
densenet DenseNet-121, from torchvision.models
rcnn FasterRCNN and MaskRCNN, model from detectron2
tsm TSM: Temporal Shift Module for Efficient Video Understanding, ICCV2019
scaled-yolov4 yolov4-csp, pytorch from WongKinYiu/ScaledYOLOv4
centernet CenterNet DLA-34, pytorch from xingyizhou/CenterNet
efficientnet EfficientNet b0-b8 and l2, pytorch from lukemelas/EfficientNet-PyTorch
detr DE⫶TR, pytorch from facebookresearch/detr

Model Zoo

The .wts files can be downloaded from model zoo for quick evaluation. But it is recommended to convert .wts from pytorch/mxnet/tensorflow model, so that you can retrain your own model.

GoogleDrive | BaiduPan pwd: uvv2

Tricky Operations

Some tricky operations encountered in these models, already solved, but might have better solutions.

Name Description
BatchNorm Implement by a scale layer, used in resnet, googlenet, mobilenet, etc.
MaxPool2d(ceil_mode=True) use a padding layer before maxpool to solve ceil_mode=True, see googlenet.
average pool with padding use setAverageCountExcludesPadding() when necessary, see inception.
relu6 use Relu6(x) = Relu(x) - Relu(x-6), see mobilenet.
torch.chunk() implement the 'chunk(2, dim=C)' by tensorrt plugin, see shufflenet.
channel shuffle use two shuffle layers to implement channel_shuffle, see shufflenet.
adaptive pool use fixed input dimension, and use regular average pooling, see shufflenet.
leaky relu I wrote a leaky relu plugin, but PRelu in NvInferPlugin.h can be used, see yolov3 in branch trt4.
yolo layer v1 yolo layer is implemented as a plugin, see yolov3 in branch trt4.
yolo layer v2 three yolo layers implemented in one plugin, see yolov3-spp.
upsample replaced by a deconvolution layer, see yolov3.
hsigmoid hard sigmoid is implemented as a plugin, hsigmoid and hswish are used in mobilenetv3
retinaface output decode implement a plugin to decode bbox, confidence and landmarks, see retinaface.
mish mish activation is implemented as a plugin, mish is used in yolov4
prelu mxnet's prelu activation with trainable gamma is implemented as a plugin, used in arcface
HardSwish hard_swish = x * hard_sigmoid, used in yolov5 v3.0
LSTM Implemented pytorch nn.LSTM() with tensorrt api

Speed Benchmark

Models Device BatchSize Mode Input Shape(HxW) FPS
YOLOv3-tiny Xeon E5-2620/GTX1080 1 FP32 608x608 333
YOLOv3(darknet53) Xeon E5-2620/GTX1080 1 FP32 608x608 39.2
YOLOv3(darknet53) Xeon E5-2620/GTX1080 1 INT8 608x608 71.4
YOLOv3-spp(darknet53) Xeon E5-2620/GTX1080 1 FP32 608x608 38.5
YOLOv4(CSPDarknet53) Xeon E5-2620/GTX1080 1 FP32 608x608 35.7
YOLOv4(CSPDarknet53) Xeon E5-2620/GTX1080 4 FP32 608x608 40.9
YOLOv4(CSPDarknet53) Xeon E5-2620/GTX1080 8 FP32 608x608 41.3
YOLOv5-s v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 142
YOLOv5-s v3.0 Xeon E5-2620/GTX1080 4 FP32 608x608 173
YOLOv5-s v3.0 Xeon E5-2620/GTX1080 8 FP32 608x608 190
YOLOv5-m v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 71
YOLOv5-l v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 43
YOLOv5-x v3.0 Xeon E5-2620/GTX1080 1 FP32 608x608 29
YOLOv5-s v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 142
YOLOv5-m v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 71
YOLOv5-l v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 40
YOLOv5-x v4.0 Xeon E5-2620/GTX1080 1 FP32 608x608 27
RetinaFace(resnet50) Xeon E5-2620/GTX1080 1 FP32 480x640 90
RetinaFace(resnet50) Xeon E5-2620/GTX1080 1 INT8 480x640 204
RetinaFace(mobilenet0.25) Xeon E5-2620/GTX1080 1 FP32 480x640 417
ArcFace(LResNet50E-IR) Xeon E5-2620/GTX1080 1 FP32 112x112 333
CRNN Xeon E5-2620/GTX1080 1 FP32 32x100 1000

Help wanted, if you got speed results, please add an issue or PR.

Acknowledgments & Contact

Any contributions, questions and discussions are welcomed, contact me by following info.

E-mail: [email protected]

WeChat ID: wangxinyu0375 (可加我微信进tensorrtx交流群,备注:tensorrtx)

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